让机器人像人一样持续思考并自主编程，实现复杂环境下的自主行动。
Towards Humanoid Robot Autonomy: A Dynamic Architecture Integrating Continuous thought Machines (CTM) and Model Context Protocol (MCP)
- 构建连续思维机与模型上下文协议的动态架构，支持持续推理与自主编码。
- 在九轮实验中，七项指标表现优异，任务成功率超预期。
- 适合研究具身智能、自主机器人系统的人参考其动态决策框架。
为解决人形机器人在陌生场景中静态预设的‘思考-规划-执行’模式与缺乏自主编程能力导致的‘调用工具-返回结果’僵化问题，本文设计了一种连接连续思维机（CTM）与模型上下文协议（MCP）的动态架构。通过时间片并行机制与秩压缩技术实现参数抑制，提升自主行动能力。研究基于OpenAI的o4-mini-high构建仿真环境，并引入扩展版SayCan数据集，开展九轮实验。结果表明，该架构在任务成功率达87.3%、执行成功率达84.1%等七项指标上均表现良好，验证了其可行性与有效性。实践上为基于持续思考的人形机器人自主动态编程提供了可借鉴路径。
原文摘要 · Abstract (English)
To address the gaps between the static pre-set "thinking-planning-action" of humanoid robots in unfamiliar scenarios and the highly programmed "call tool-return result" due to the lack of autonomous coding capabilities, this work designs a dynamic architecture connecting continuous thought machines (CTM) and model context protocol (MCP). It proposes a theoretical parallel solution through tick-slab and uses rank compression to achieve parameter suppression to provide a solution for achieving autonomous actions due to autonomous coding. The researcher used a simulation-based experiment using OpenAI's o4-mini-high as a tool to build the experimental environment, and introduced the extended SayCan dataset to conduct nine epochs of experiments. The experimental results show that the CTM-MCP architecture is feasible and effective through the data results of seven metrics: task success rate (TSR), execution success rate (ESR), average episode length (AEL), ROSCOE, REVEAL, proficiency self-assessment (PSA), task effectiveness (TE). In practice, it provides a reference experience for exploring the autonomous dynamic coding of humanoid robots based on continuous thinking to achieve human-like autonomous actions.
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